In the previous chapter, we have learned how to estimate a parameter of interest nonparametrically using the plug-in principle. We have also seen that a plug-in estimate may be biased: for example, the plug-in estimate of the variance is biased. In this chapter, we will learn how to reduce the bias of a biased estimate using the so-called jackknife method. The jackknife method was originally proposed by Maurice Quenouille, a British statistician of French ancestry, as a nonparametric method for estimating the bias of an estimate. In 1958, John Tukey extended the method by showing how to use it not only for estimating, but also for reducing the bias, and coined the name “jackknife.” As a pocket knife, this technique can be used as a “quick and dirty” tool for solving a variety of statistical problems.